Adding & Removing Columns

In [1]:

import pandas as pdhouses = pd.read_csv("data/kc_house_data.csv")titanic = pd.read_csv("data/titanic.csv")netflix = pd.read_csv("data/netflix_titles.csv", sep="|", index_col=0)btc = pd.read_csv("data/coin_Bitcoin.csv")countries = pd.read_csv("data/world-happiness-report-2021.csv")

In [2]:

countries.set_index("Country name", inplace=True)

Dropping Rows/Cols

In [3]:

btc.drop(labels="Symbol", axis=1)

Out[3]:

 

SNo

Name

Date

High

Low

Open

Close

Volume

Marketcap

0

1

Bitcoin

2013-04-29 23:59:59

147.488007

134.000000

134.444000

144.539993

0.000000e+00

1.603769e+09

1

2

Bitcoin

2013-04-30 23:59:59

146.929993

134.050003

144.000000

139.000000

0.000000e+00

1.542813e+09

2

3

Bitcoin

2013-05-01 23:59:59

139.889999

107.720001

139.000000

116.989998

0.000000e+00

1.298955e+09

3

4

Bitcoin

2013-05-02 23:59:59

125.599998

92.281898

116.379997

105.209999

0.000000e+00

1.168517e+09

4

5

Bitcoin

2013-05-03 23:59:59

108.127998

79.099998

106.250000

97.750000

0.000000e+00

1.085995e+09

...

...

...

...

...

...

...

...

...

...

2986

2987

Bitcoin

2021-07-02 23:59:59

33939.588699

32770.680780

33549.600177

33897.048590

3.872897e+10

6.354508e+11

2987

2988

Bitcoin

2021-07-03 23:59:59

34909.259899

33402.696536

33854.421362

34668.548402

2.438396e+10

6.499397e+11

2988

2989

Bitcoin

2021-07-04 23:59:59

35937.567147

34396.477458

34665.564866

35287.779766

2.492431e+10

6.615748e+11

2989

2990

Bitcoin

2021-07-05 23:59:59

35284.344430

33213.661034

35284.344430

33746.002456

2.672155e+10

6.326962e+11

2990

2991

Bitcoin

2021-07-06 23:59:59

35038.536363

33599.916169

33723.509655

34235.193451

2.650126e+10

6.418992e+11

2991 rows × 9 columns

In [4]:

btc

Out[4]:

 

SNo

Name

Symbol

Date

High

Low

Open

Close

Volume

Marketcap

0

1

Bitcoin

BTC

2013-04-29 23:59:59

147.488007

134.000000

134.444000

144.539993

0.000000e+00

1.603769e+09

1

2

Bitcoin

BTC

2013-04-30 23:59:59

146.929993

134.050003

144.000000

139.000000

0.000000e+00

1.542813e+09

2

3

Bitcoin

BTC

2013-05-01 23:59:59

139.889999

107.720001

139.000000

116.989998

0.000000e+00

1.298955e+09

3

4

Bitcoin

BTC

2013-05-02 23:59:59

125.599998

92.281898

116.379997

105.209999

0.000000e+00

1.168517e+09

4

5

Bitcoin

BTC

2013-05-03 23:59:59

108.127998

79.099998

106.250000

97.750000

0.000000e+00

1.085995e+09

...

...

...

...

...

...

...

...

...

...

...

2986

2987

Bitcoin

BTC

2021-07-02 23:59:59

33939.588699

32770.680780

33549.600177

33897.048590

3.872897e+10

6.354508e+11

2987

2988

Bitcoin

BTC

2021-07-03 23:59:59

34909.259899

33402.696536

33854.421362

34668.548402

2.438396e+10

6.499397e+11

2988

2989

Bitcoin

BTC

2021-07-04 23:59:59

35937.567147

34396.477458

34665.564866

35287.779766

2.492431e+10

6.615748e+11

2989

2990

Bitcoin

BTC

2021-07-05 23:59:59

35284.344430

33213.661034

35284.344430

33746.002456

2.672155e+10

6.326962e+11

2990

2991

Bitcoin

BTC

2021-07-06 23:59:59

35038.536363

33599.916169

33723.509655

34235.193451

2.650126e+10

6.418992e+11

2991 rows × 10 columns

In [5]:

btc.drop(labels=["SNo", "Name", "Symbol"], axis='columns')

Out[5]:

 

Date

High

Low

Open

Close

Volume

Marketcap

0

2013-04-29 23:59:59

147.488007

134.000000

134.444000

144.539993

0.000000e+00

1.603769e+09

1

2013-04-30 23:59:59

146.929993

134.050003

144.000000

139.000000

0.000000e+00

1.542813e+09

2

2013-05-01 23:59:59

139.889999

107.720001

139.000000

116.989998

0.000000e+00

1.298955e+09

3

2013-05-02 23:59:59

125.599998

92.281898

116.379997

105.209999

0.000000e+00

1.168517e+09

4

2013-05-03 23:59:59

108.127998

79.099998

106.250000

97.750000

0.000000e+00

1.085995e+09

...

...

...

...

...

...

...

...

2986

2021-07-02 23:59:59

33939.588699

32770.680780

33549.600177

33897.048590

3.872897e+10

6.354508e+11

2987

2021-07-03 23:59:59

34909.259899

33402.696536

33854.421362

34668.548402

2.438396e+10

6.499397e+11

2988

2021-07-04 23:59:59

35937.567147

34396.477458

34665.564866

35287.779766

2.492431e+10

6.615748e+11

2989

2021-07-05 23:59:59

35284.344430

33213.661034

35284.344430

33746.002456

2.672155e+10

6.326962e+11

2990

2021-07-06 23:59:59

35038.536363

33599.916169

33723.509655

34235.193451

2.650126e+10

6.418992e+11

2991 rows × 7 columns

In [6]:

# btc.drop(labels=["SNo", "Name", "Symbol"], axis='columns')btc.drop(columns=["SNo", "Name", "Symbol"])

Out[6]:

 

Date

High

Low

Open

Close

Volume

Marketcap

0

2013-04-29 23:59:59

147.488007

134.000000

134.444000

144.539993

0.000000e+00

1.603769e+09

1

2013-04-30 23:59:59

146.929993

134.050003

144.000000

139.000000

0.000000e+00

1.542813e+09

2

2013-05-01 23:59:59

139.889999

107.720001

139.000000

116.989998

0.000000e+00

1.298955e+09

3

2013-05-02 23:59:59

125.599998

92.281898

116.379997

105.209999

0.000000e+00

1.168517e+09

4

2013-05-03 23:59:59

108.127998

79.099998

106.250000

97.750000

0.000000e+00

1.085995e+09

...

...

...

...

...

...

...

...

2986

2021-07-02 23:59:59

33939.588699

32770.680780

33549.600177

33897.048590

3.872897e+10

6.354508e+11

2987

2021-07-03 23:59:59

34909.259899

33402.696536

33854.421362

34668.548402

2.438396e+10

6.499397e+11

2988

2021-07-04 23:59:59

35937.567147

34396.477458

34665.564866

35287.779766

2.492431e+10

6.615748e+11

2989

2021-07-05 23:59:59

35284.344430

33213.661034

35284.344430

33746.002456

2.672155e+10

6.326962e+11

2990

2021-07-06 23:59:59

35038.536363

33599.916169

33723.509655

34235.193451

2.650126e+10

6.418992e+11

2991 rows × 7 columns

In [7]:

btc[["Date", "High", "Low"]]

Out[7]:

 

Date

High

Low

0

2013-04-29 23:59:59

147.488007

134.000000

1

2013-04-30 23:59:59

146.929993

134.050003

2

2013-05-01 23:59:59

139.889999

107.720001

3

2013-05-02 23:59:59

125.599998

92.281898

4

2013-05-03 23:59:59

108.127998

79.099998

...

...

...

...

2986

2021-07-02 23:59:59

33939.588699

32770.680780

2987

2021-07-03 23:59:59

34909.259899

33402.696536

2988

2021-07-04 23:59:59

35937.567147

34396.477458

2989

2021-07-05 23:59:59

35284.344430

33213.661034

2990

2021-07-06 23:59:59

35038.536363

33599.916169

2991 rows × 3 columns

In [8]:

btc.drop(columns=["SNo", "Name", "Symbol"], inplace=True)

In [9]:

btc

Out[9]:

 

Date

High

Low

Open

Close

Volume

Marketcap

0

2013-04-29 23:59:59

147.488007

134.000000

134.444000

144.539993

0.000000e+00

1.603769e+09

1

2013-04-30 23:59:59

146.929993

134.050003

144.000000

139.000000

0.000000e+00

1.542813e+09

2

2013-05-01 23:59:59

139.889999

107.720001

139.000000

116.989998

0.000000e+00

1.298955e+09

3

2013-05-02 23:59:59

125.599998

92.281898

116.379997

105.209999

0.000000e+00

1.168517e+09

4

2013-05-03 23:59:59

108.127998

79.099998

106.250000

97.750000

0.000000e+00

1.085995e+09

...

...

...

...

...

...

...

...

2986

2021-07-02 23:59:59

33939.588699

32770.680780

33549.600177

33897.048590

3.872897e+10

6.354508e+11

2987

2021-07-03 23:59:59

34909.259899

33402.696536

33854.421362

34668.548402

2.438396e+10

6.499397e+11

2988

2021-07-04 23:59:59

35937.567147

34396.477458

34665.564866

35287.779766

2.492431e+10

6.615748e+11

2989

2021-07-05 23:59:59

35284.344430

33213.661034

35284.344430

33746.002456

2.672155e+10

6.326962e+11

2990

2021-07-06 23:59:59

35038.536363

33599.916169

33723.509655

34235.193451

2.650126e+10

6.418992e+11

2991 rows × 7 columns

In [10]:

countries

Out[10]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Finland

Western Europe

7.842

0.032

7.904

7.780

10.775

0.954

72.000

0.949

-0.098

0.186

2.43

1.446

1.106

0.741

0.691

0.124

0.481

3.253

Denmark

Western Europe

7.620

0.035

7.687

7.552

10.933

0.954

72.700

0.946

0.030

0.179

2.43

1.502

1.108

0.763

0.686

0.208

0.485

2.868

Switzerland

Western Europe

7.571

0.036

7.643

7.500

11.117

0.942

74.400

0.919

0.025

0.292

2.43

1.566

1.079

0.816

0.653

0.204

0.413

2.839

Iceland

Western Europe

7.554

0.059

7.670

7.438

10.878

0.983

73.000

0.955

0.160

0.673

2.43

1.482

1.172

0.772

0.698

0.293

0.170

2.967

Netherlands

Western Europe

7.464

0.027

7.518

7.410

10.932

0.942

72.400

0.913

0.175

0.338

2.43

1.501

1.079

0.753

0.647

0.302

0.384

2.798

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

Lesotho

Sub-Saharan Africa

3.512

0.120

3.748

3.276

7.926

0.787

48.700

0.715

-0.131

0.915

2.43

0.451

0.731

0.007

0.405

0.103

0.015

1.800

Botswana

Sub-Saharan Africa

3.467

0.074

3.611

3.322

9.782

0.784

59.269

0.824

-0.246

0.801

2.43

1.099

0.724

0.340

0.539

0.027

0.088

0.648

Rwanda

Sub-Saharan Africa

3.415

0.068

3.548

3.282

7.676

0.552

61.400

0.897

0.061

0.167

2.43

0.364

0.202

0.407

0.627

0.227

0.493

1.095

Zimbabwe

Sub-Saharan Africa

3.145

0.058

3.259

3.030

7.943

0.750

56.201

0.677

-0.047

0.821

2.43

0.457

0.649

0.243

0.359

0.157

0.075

1.205

Afghanistan

South Asia

2.523

0.038

2.596

2.449

7.695

0.463

52.493

0.382

-0.102

0.924

2.43

0.370

0.000

0.126

0.000

0.122

0.010

1.895

149 rows × 19 columns

In [11]:

countries.drop(labels="Denmark", axis=0)

Out[11]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Finland

Western Europe

7.842

0.032

7.904

7.780

10.775

0.954

72.000

0.949

-0.098

0.186

2.43

1.446

1.106

0.741

0.691

0.124

0.481

3.253

Switzerland

Western Europe

7.571

0.036

7.643

7.500

11.117

0.942

74.400

0.919

0.025

0.292

2.43

1.566

1.079

0.816

0.653

0.204

0.413

2.839

Iceland

Western Europe

7.554

0.059

7.670

7.438

10.878

0.983

73.000

0.955

0.160

0.673

2.43

1.482

1.172

0.772

0.698

0.293

0.170

2.967

Netherlands

Western Europe

7.464

0.027

7.518

7.410

10.932

0.942

72.400

0.913

0.175

0.338

2.43

1.501

1.079

0.753

0.647

0.302

0.384

2.798

Norway

Western Europe

7.392

0.035

7.462

7.323

11.053

0.954

73.300

0.960

0.093

0.270

2.43

1.543

1.108

0.782

0.703

0.249

0.427

2.580

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

Lesotho

Sub-Saharan Africa

3.512

0.120

3.748

3.276

7.926

0.787

48.700

0.715

-0.131

0.915

2.43

0.451

0.731

0.007

0.405

0.103

0.015

1.800

Botswana

Sub-Saharan Africa

3.467

0.074

3.611

3.322

9.782

0.784

59.269

0.824

-0.246

0.801

2.43

1.099

0.724

0.340

0.539

0.027

0.088

0.648

Rwanda

Sub-Saharan Africa

3.415

0.068

3.548

3.282

7.676

0.552

61.400

0.897

0.061

0.167

2.43

0.364

0.202

0.407

0.627

0.227

0.493

1.095

Zimbabwe

Sub-Saharan Africa

3.145

0.058

3.259

3.030

7.943

0.750

56.201

0.677

-0.047

0.821

2.43

0.457

0.649

0.243

0.359

0.157

0.075

1.205

Afghanistan

South Asia

2.523

0.038

2.596

2.449

7.695

0.463

52.493

0.382

-0.102

0.924

2.43

0.370

0.000

0.126

0.000

0.122

0.010

1.895

148 rows × 19 columns

In [12]:

countries

Out[12]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Finland

Western Europe

7.842

0.032

7.904

7.780

10.775

0.954

72.000

0.949

-0.098

0.186

2.43

1.446

1.106

0.741

0.691

0.124

0.481

3.253

Denmark

Western Europe

7.620

0.035

7.687

7.552

10.933

0.954

72.700

0.946

0.030

0.179

2.43

1.502

1.108

0.763

0.686

0.208

0.485

2.868

Switzerland

Western Europe

7.571

0.036

7.643

7.500

11.117

0.942

74.400

0.919

0.025

0.292

2.43

1.566

1.079

0.816

0.653

0.204

0.413

2.839

Iceland

Western Europe

7.554

0.059

7.670

7.438

10.878

0.983

73.000

0.955

0.160

0.673

2.43

1.482

1.172

0.772

0.698

0.293

0.170

2.967

Netherlands

Western Europe

7.464

0.027

7.518

7.410

10.932

0.942

72.400

0.913

0.175

0.338

2.43

1.501

1.079

0.753

0.647

0.302

0.384

2.798

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

Lesotho

Sub-Saharan Africa

3.512

0.120

3.748

3.276

7.926

0.787

48.700

0.715

-0.131

0.915

2.43

0.451

0.731

0.007

0.405

0.103

0.015

1.800

Botswana

Sub-Saharan Africa

3.467

0.074

3.611

3.322

9.782

0.784

59.269

0.824

-0.246

0.801

2.43

1.099

0.724

0.340

0.539

0.027

0.088

0.648

Rwanda

Sub-Saharan Africa

3.415

0.068

3.548

3.282

7.676

0.552

61.400

0.897

0.061

0.167

2.43

0.364

0.202

0.407

0.627

0.227

0.493

1.095

Zimbabwe

Sub-Saharan Africa

3.145

0.058

3.259

3.030

7.943

0.750

56.201

0.677

-0.047

0.821

2.43

0.457

0.649

0.243

0.359

0.157

0.075

1.205

Afghanistan

South Asia

2.523

0.038

2.596

2.449

7.695

0.463

52.493

0.382

-0.102

0.924

2.43

0.370

0.000

0.126

0.000

0.122

0.010

1.895

149 rows × 19 columns

In [13]:

countries.drop(["Denmark", "Iceland", "Finland"])

Out[13]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Switzerland

Western Europe

7.571

0.036

7.643

7.500

11.117

0.942

74.400

0.919

0.025

0.292

2.43

1.566

1.079

0.816

0.653

0.204

0.413

2.839

Netherlands

Western Europe

7.464

0.027

7.518

7.410

10.932

0.942

72.400

0.913

0.175

0.338

2.43

1.501

1.079

0.753

0.647

0.302

0.384

2.798

Norway

Western Europe

7.392

0.035

7.462

7.323

11.053

0.954

73.300

0.960

0.093

0.270

2.43

1.543

1.108

0.782

0.703

0.249

0.427

2.580

Sweden

Western Europe

7.363

0.036

7.433

7.293

10.867

0.934

72.700

0.945

0.086

0.237

2.43

1.478

1.062

0.763

0.685

0.244

0.448

2.683

Luxembourg

Western Europe

7.324

0.037

7.396

7.252

11.647

0.908

72.600

0.907

-0.034

0.386

2.43

1.751

1.003

0.760

0.639

0.166

0.353

2.653

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

Lesotho

Sub-Saharan Africa

3.512

0.120

3.748

3.276

7.926

0.787

48.700

0.715

-0.131

0.915

2.43

0.451

0.731

0.007

0.405

0.103

0.015

1.800

Botswana

Sub-Saharan Africa

3.467

0.074

3.611

3.322

9.782

0.784

59.269

0.824

-0.246

0.801

2.43

1.099

0.724

0.340

0.539

0.027

0.088

0.648

Rwanda

Sub-Saharan Africa

3.415

0.068

3.548

3.282

7.676

0.552

61.400

0.897

0.061

0.167

2.43

0.364

0.202

0.407

0.627

0.227

0.493

1.095

Zimbabwe

Sub-Saharan Africa

3.145

0.058

3.259

3.030

7.943

0.750

56.201

0.677

-0.047

0.821

2.43

0.457

0.649

0.243

0.359

0.157

0.075

1.205

Afghanistan

South Asia

2.523

0.038

2.596

2.449

7.695

0.463

52.493

0.382

-0.102

0.924

2.43

0.370

0.000

0.126

0.000

0.122

0.010

1.895

146 rows × 19 columns

In [14]:

btc.sort_index(ascending=False, inplace=True)btc.drop(2990)

Out[14]:

 

Date

High

Low

Open

Close

Volume

Marketcap

2989

2021-07-05 23:59:59

35284.344430

33213.661034

35284.344430

33746.002456

2.672155e+10

6.326962e+11

2988

2021-07-04 23:59:59

35937.567147

34396.477458

34665.564866

35287.779766

2.492431e+10

6.615748e+11

2987

2021-07-03 23:59:59

34909.259899

33402.696536

33854.421362

34668.548402

2.438396e+10

6.499397e+11

2986

2021-07-02 23:59:59

33939.588699

32770.680780

33549.600177

33897.048590

3.872897e+10

6.354508e+11

2985

2021-07-01 23:59:59

35035.982712

32883.781226

35035.982712

33572.117653

3.783896e+10

6.293393e+11

...

...

...

...

...

...

...

...

4

2013-05-03 23:59:59

108.127998

79.099998

106.250000

97.750000

0.000000e+00

1.085995e+09

3

2013-05-02 23:59:59

125.599998

92.281898

116.379997

105.209999

0.000000e+00

1.168517e+09

2

2013-05-01 23:59:59

139.889999

107.720001

139.000000

116.989998

0.000000e+00

1.298955e+09

1

2013-04-30 23:59:59

146.929993

134.050003

144.000000

139.000000

0.000000e+00

1.542813e+09

0

2013-04-29 23:59:59

147.488007

134.000000

134.444000

144.539993

0.000000e+00

1.603769e+09

2990 rows × 7 columns

In [15]:

countries

Out[15]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Finland

Western Europe

7.842

0.032

7.904

7.780

10.775

0.954

72.000

0.949

-0.098

0.186

2.43

1.446

1.106

0.741

0.691

0.124

0.481

3.253

Denmark

Western Europe

7.620

0.035

7.687

7.552

10.933

0.954

72.700

0.946

0.030

0.179

2.43

1.502

1.108

0.763

0.686

0.208

0.485

2.868

Switzerland

Western Europe

7.571

0.036

7.643

7.500

11.117

0.942

74.400

0.919

0.025

0.292

2.43

1.566

1.079

0.816

0.653

0.204

0.413

2.839

Iceland

Western Europe

7.554

0.059

7.670

7.438

10.878

0.983

73.000

0.955

0.160

0.673

2.43

1.482

1.172

0.772

0.698

0.293

0.170

2.967

Netherlands

Western Europe

7.464

0.027

7.518

7.410

10.932

0.942

72.400

0.913

0.175

0.338

2.43

1.501

1.079

0.753

0.647

0.302

0.384

2.798

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

Lesotho

Sub-Saharan Africa

3.512

0.120

3.748

3.276

7.926

0.787

48.700

0.715

-0.131

0.915

2.43

0.451

0.731

0.007

0.405

0.103

0.015

1.800

Botswana

Sub-Saharan Africa

3.467

0.074

3.611

3.322

9.782

0.784

59.269

0.824

-0.246

0.801

2.43

1.099

0.724

0.340

0.539

0.027

0.088

0.648

Rwanda

Sub-Saharan Africa

3.415

0.068

3.548

3.282

7.676

0.552

61.400

0.897

0.061

0.167

2.43

0.364

0.202

0.407

0.627

0.227

0.493

1.095

Zimbabwe

Sub-Saharan Africa

3.145

0.058

3.259

3.030

7.943

0.750

56.201

0.677

-0.047

0.821

2.43

0.457

0.649

0.243

0.359

0.157

0.075

1.205

Afghanistan

South Asia

2.523

0.038

2.596

2.449

7.695

0.463

52.493

0.382

-0.102

0.924

2.43

0.370

0.000

0.126

0.000

0.122

0.010

1.895

149 rows × 19 columns

In [16]:

countries.drop(countries.index[0])

Out[16]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Denmark

Western Europe

7.620

0.035

7.687

7.552

10.933

0.954

72.700

0.946

0.030

0.179

2.43

1.502

1.108

0.763

0.686

0.208

0.485

2.868

Switzerland

Western Europe

7.571

0.036

7.643

7.500

11.117

0.942

74.400

0.919

0.025

0.292

2.43

1.566

1.079

0.816

0.653

0.204

0.413

2.839

Iceland

Western Europe

7.554

0.059

7.670

7.438

10.878

0.983

73.000

0.955

0.160

0.673

2.43

1.482

1.172

0.772

0.698

0.293

0.170

2.967

Netherlands

Western Europe

7.464

0.027

7.518

7.410

10.932

0.942

72.400

0.913

0.175

0.338

2.43

1.501

1.079

0.753

0.647

0.302

0.384

2.798

Norway

Western Europe

7.392

0.035

7.462

7.323

11.053

0.954

73.300

0.960

0.093

0.270

2.43

1.543

1.108

0.782

0.703

0.249

0.427

2.580

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

Lesotho

Sub-Saharan Africa

3.512

0.120

3.748

3.276

7.926

0.787

48.700

0.715

-0.131

0.915

2.43

0.451

0.731

0.007

0.405

0.103

0.015

1.800

Botswana

Sub-Saharan Africa

3.467

0.074

3.611

3.322

9.782

0.784

59.269

0.824

-0.246

0.801

2.43

1.099

0.724

0.340

0.539

0.027

0.088

0.648

Rwanda

Sub-Saharan Africa

3.415

0.068

3.548

3.282

7.676

0.552

61.400

0.897

0.061

0.167

2.43

0.364

0.202

0.407

0.627

0.227

0.493

1.095

Zimbabwe

Sub-Saharan Africa

3.145

0.058

3.259

3.030

7.943

0.750

56.201

0.677

-0.047

0.821

2.43

0.457

0.649

0.243

0.359

0.157

0.075

1.205

Afghanistan

South Asia

2.523

0.038

2.596

2.449

7.695

0.463

52.493

0.382

-0.102

0.924

2.43

0.370

0.000

0.126

0.000

0.122

0.010

1.895

148 rows × 19 columns

In [17]:

countries.drop(countries.index[10:])

Out[17]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Finland

Western Europe

7.842

0.032

7.904

7.780

10.775

0.954

72.0

0.949

-0.098

0.186

2.43

1.446

1.106

0.741

0.691

0.124

0.481

3.253

Denmark

Western Europe

7.620

0.035

7.687

7.552

10.933

0.954

72.7

0.946

0.030

0.179

2.43

1.502

1.108

0.763

0.686

0.208

0.485

2.868

Switzerland

Western Europe

7.571

0.036

7.643

7.500

11.117

0.942

74.4

0.919

0.025

0.292

2.43

1.566

1.079

0.816

0.653

0.204

0.413

2.839

Iceland

Western Europe

7.554

0.059

7.670

7.438

10.878

0.983

73.0

0.955

0.160

0.673

2.43

1.482

1.172

0.772

0.698

0.293

0.170

2.967

Netherlands

Western Europe

7.464

0.027

7.518

7.410

10.932

0.942

72.4

0.913

0.175

0.338

2.43

1.501

1.079

0.753

0.647

0.302

0.384

2.798

Norway

Western Europe

7.392

0.035

7.462

7.323

11.053

0.954

73.3

0.960

0.093

0.270

2.43

1.543

1.108

0.782

0.703

0.249

0.427

2.580

Sweden

Western Europe

7.363

0.036

7.433

7.293

10.867

0.934

72.7

0.945

0.086

0.237

2.43

1.478

1.062

0.763

0.685

0.244

0.448

2.683

Luxembourg

Western Europe

7.324

0.037

7.396

7.252

11.647

0.908

72.6

0.907

-0.034

0.386

2.43

1.751

1.003

0.760

0.639

0.166

0.353

2.653

New Zealand

North America and ANZ

7.277

0.040

7.355

7.198

10.643

0.948

73.4

0.929

0.134

0.242

2.43

1.400

1.094

0.785

0.665

0.276

0.445

2.612

Austria

Western Europe

7.268

0.036

7.337

7.198

10.906

0.934

73.3

0.908

0.042

0.481

2.43

1.492

1.062

0.782

0.640

0.215

0.292

2.784

Adding Columns

In [18]:

titanic["species"] = "human"titanic

Out[18]:

 

pclass

survived

name

sex

age

sibsp

parch

ticket

fare

cabin

embarked

boat

body

home.dest

species

0

1

1

Allen, Miss. Elisabeth Walton

female

29

0

0

24160

211.3375

B5

S

2

?

St Louis, MO

human

1

1

1

Allison, Master. Hudson Trevor

male

0.9167

1

2

113781

151.55

C22 C26

S

11

?

Montreal, PQ / Chesterville, ON

human

2

1

0

Allison, Miss. Helen Loraine

female

2

1

2

113781

151.55

C22 C26

S

?

?

Montreal, PQ / Chesterville, ON

human

3

1

0

Allison, Mr. Hudson Joshua Creighton

male

30

1

2

113781

151.55

C22 C26

S

?

135

Montreal, PQ / Chesterville, ON

human

4

1

0

Allison, Mrs. Hudson J C (Bessie Waldo Daniels)

female

25

1

2

113781

151.55

C22 C26

S

?

?

Montreal, PQ / Chesterville, ON

human

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

1304

3

0

Zabour, Miss. Hileni

female

14.5

1

0

2665

14.4542

?

C

?

328

?

human

1305

3

0

Zabour, Miss. Thamine

female

?

1

0

2665

14.4542

?

C

?

?

?

human

1306

3

0

Zakarian, Mr. Mapriededer

male

26.5

0

0

2656

7.225

?

C

?

304

?

human

1307

3

0

Zakarian, Mr. Ortin

male

27

0

0

2670

7.225

?

C

?

?

?

human

1308

3

0

Zimmerman, Mr. Leo

male

29

0

0

315082

7.875

?

S

?

?

?

human

1309 rows × 15 columns

In [19]:

houses.insert(0, "county", "King County")

In [20]:

houses

Out[20]:

 

county

id

date

price

bedrooms

bathrooms

sqft_living

sqft_lot

floors

waterfront

...

grade

sqft_above

sqft_basement

yr_built

yr_renovated

zipcode

lat

long

sqft_living15

sqft_lot15

0

King County

7129300520

20141013T000000

221900.0

3

1.00

1180

5650

1.0

0

...

7

1180

0

1955

0

98178

47.5112

-122.257

1340

5650

1

King County

6414100192

20141209T000000

538000.0

3

2.25

2570

7242

2.0

0

...

7

2170

400

1951

1991

98125

47.7210

-122.319

1690

7639

2

King County

5631500400

20150225T000000

180000.0

2

1.00

770

10000

1.0

0

...

6

770

0

1933

0

98028

47.7379

-122.233

2720

8062

3

King County

2487200875

20141209T000000

604000.0

4

3.00

1960

5000

1.0

0

...

7

1050

910

1965

0

98136

47.5208

-122.393

1360

5000

4

King County

1954400510

20150218T000000

510000.0

3

2.00

1680

8080

1.0

0

...

8

1680

0

1987

0

98074

47.6168

-122.045

1800

7503

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

21608

King County

263000018

20140521T000000

360000.0

3

2.50

1530

1131

3.0

0

...

8

1530

0

2009

0

98103

47.6993

-122.346

1530

1509

21609

King County

6600060120

20150223T000000

400000.0

4

2.50

2310

5813

2.0

0

...

8

2310

0

2014

0

98146

47.5107

-122.362

1830

7200

21610

King County

1523300141

20140623T000000

402101.0

2

0.75

1020

1350

2.0

0

...

7

1020

0

2009

0

98144

47.5944

-122.299

1020

2007

21611

King County

291310100

20150116T000000

400000.0

3

2.50

1600

2388

2.0

0

...

8

1600

0

2004

0

98027

47.5345

-122.069

1410

1287

21612

King County

1523300157

20141015T000000

325000.0

2

0.75

1020

1076

2.0

0

...

7

1020

0

2008

0

98144

47.5941

-122.299

1020

1357

21613 rows × 22 columns

In [21]:

houses["sale_price"] = houses["price"]

In [22]:

houses

Out[22]:

 

county

id

date

price

bedrooms

bathrooms

sqft_living

sqft_lot

floors

waterfront

...

sqft_above

sqft_basement

yr_built

yr_renovated

zipcode

lat

long

sqft_living15

sqft_lot15

sale_price

0

King County

7129300520

20141013T000000

221900.0

3

1.00

1180

5650

1.0

0

...

1180

0

1955

0

98178

47.5112

-122.257

1340

5650

221900.0

1

King County

6414100192

20141209T000000

538000.0

3

2.25

2570

7242

2.0

0

...

2170

400

1951

1991

98125

47.7210

-122.319

1690

7639

538000.0

2

King County

5631500400

20150225T000000

180000.0

2

1.00

770

10000

1.0

0

...

770

0

1933

0

98028

47.7379

-122.233

2720

8062

180000.0

3

King County

2487200875

20141209T000000

604000.0

4

3.00

1960

5000

1.0

0

...

1050

910

1965

0

98136

47.5208

-122.393

1360

5000

604000.0

4

King County

1954400510

20150218T000000

510000.0

3

2.00

1680

8080

1.0

0

...

1680

0

1987

0

98074

47.6168

-122.045

1800

7503

510000.0

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

21608

King County

263000018

20140521T000000

360000.0

3

2.50

1530

1131

3.0

0

...

1530

0

2009

0

98103

47.6993

-122.346

1530

1509

360000.0

21609

King County

6600060120

20150223T000000

400000.0

4

2.50

2310

5813

2.0

0

...

2310

0

2014

0

98146

47.5107

-122.362

1830

7200

400000.0

21610

King County

1523300141

20140623T000000

402101.0

2

0.75

1020

1350

2.0

0

...

1020

0

2009

0

98144

47.5944

-122.299

1020

2007

402101.0

21611

King County

291310100

20150116T000000

400000.0

3

2.50

1600

2388

2.0

0

...

1600

0

2004

0

98027

47.5345

-122.069

1410

1287

400000.0

21612

King County

1523300157

20141015T000000

325000.0

2

0.75

1020

1076

2.0

0

...

1020

0

2008

0

98144

47.5941

-122.299

1020

1357

325000.0

21613 rows × 23 columns

In [23]:

houses.insert(3, "num_bedrooms", houses["bedrooms"])

In [24]:

houses

Out[24]:

 

county

id

date

num_bedrooms

price

bedrooms

bathrooms

sqft_living

sqft_lot

floors

...

sqft_above

sqft_basement

yr_built

yr_renovated

zipcode

lat

long

sqft_living15

sqft_lot15

sale_price

0

King County

7129300520

20141013T000000

3

221900.0

3

1.00

1180

5650

1.0

...

1180

0

1955

0

98178

47.5112

-122.257

1340

5650

221900.0

1

King County

6414100192

20141209T000000

3

538000.0

3

2.25

2570

7242

2.0

...

2170

400

1951

1991

98125

47.7210

-122.319

1690

7639

538000.0

2

King County

5631500400

20150225T000000

2

180000.0

2

1.00

770

10000

1.0

...

770

0

1933

0

98028

47.7379

-122.233

2720

8062

180000.0

3

King County

2487200875

20141209T000000

4

604000.0

4

3.00

1960

5000

1.0

...

1050

910

1965

0

98136

47.5208

-122.393

1360

5000

604000.0

4

King County

1954400510

20150218T000000

3

510000.0

3

2.00

1680

8080

1.0

...

1680

0

1987

0

98074

47.6168

-122.045

1800

7503

510000.0

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

21608

King County

263000018

20140521T000000

3

360000.0

3

2.50

1530

1131

3.0

...

1530

0

2009

0

98103

47.6993

-122.346

1530

1509

360000.0

21609

King County

6600060120

20150223T000000

4

400000.0

4

2.50

2310

5813

2.0

...

2310

0

2014

0

98146

47.5107

-122.362

1830

7200

400000.0

21610

King County

1523300141

20140623T000000

2

402101.0

2

0.75

1020

1350

2.0

...

1020

0

2009

0

98144

47.5944

-122.299

1020

2007

402101.0

21611

King County

291310100

20150116T000000

3

400000.0

3

2.50

1600

2388

2.0

...

1600

0

2004

0

98027

47.5345

-122.069

1410

1287

400000.0

21612

King County

1523300157

20141015T000000

2

325000.0

2

0.75

1020

1076

2.0

...

1020

0

2008

0

98144

47.5941

-122.299

1020

1357

325000.0

21613 rows × 24 columns

In [25]:

titanic.drop(columns=["species"], inplace=True)

In [26]:

titanic["sibsp"]

Out[26]:

0       0

1       1

2       1

3       1

4       1

       ..

1304    1

1305    1

1306    0

1307    0

1308    0

Name: sibsp, Length: 1309, dtype: int64

In [27]:

titanic["parch"]

Out[27]:

0       0

1       2

2       2

3       2

4       2

       ..

1304    0

1305    0

1306    0

1307    0

1308    0

Name: parch, Length: 1309, dtype: int64

In [28]:

titanic["sibsp"] + titanic["parch"]

Out[28]:

0       0

1       3

2       3

3       3

4       3

       ..

1304    1

1305    1

1306    0

1307    0

1308    0

Length: 1309, dtype: int64

In [29]:

titanic["num_relatives"] = titanic["sibsp"] + titanic["parch"]

In [30]:

titanic

Out[30]:

 

pclass

survived

name

sex

age

sibsp

parch

ticket

fare

cabin

embarked

boat

body

home.dest

num_relatives

0

1

1

Allen, Miss. Elisabeth Walton

female

29

0

0

24160

211.3375

B5

S

2

?

St Louis, MO

0

1

1

1

Allison, Master. Hudson Trevor

male

0.9167

1

2

113781

151.55

C22 C26

S

11

?

Montreal, PQ / Chesterville, ON

3

2

1

0

Allison, Miss. Helen Loraine

female

2

1

2

113781

151.55

C22 C26

S

?

?

Montreal, PQ / Chesterville, ON

3

3

1

0

Allison, Mr. Hudson Joshua Creighton

male

30

1

2

113781

151.55

C22 C26

S

?

135

Montreal, PQ / Chesterville, ON

3

4

1

0

Allison, Mrs. Hudson J C (Bessie Waldo Daniels)

female

25

1

2

113781

151.55

C22 C26

S

?

?

Montreal, PQ / Chesterville, ON

3

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

1304

3

0

Zabour, Miss. Hileni

female

14.5

1

0

2665

14.4542

?

C

?

328

?

1

1305

3

0

Zabour, Miss. Thamine

female

?

1

0

2665

14.4542

?

C

?

?

?

1

1306

3

0

Zakarian, Mr. Mapriededer

male

26.5

0

0

2656

7.225

?

C

?

304

?

0

1307

3

0

Zakarian, Mr. Ortin

male

27

0

0

2670

7.225

?

C

?

?

?

0

1308

3

0

Zimmerman, Mr. Leo

male

29

0

0

315082

7.875

?

S

?

?

?

0

1309 rows × 15 columns

In [31]:

titanic.sort_values("num_relatives", ascending=False)

Out[31]:

 

pclass

survived

name

sex

age

sibsp

parch

ticket

fare

cabin

embarked

boat

body

home.dest

num_relatives

1177

3

0

Sage, Mr. Frederick

male

?

8

2

CA. 2343

69.55

?

S

?

?

?

10

1179

3

0

Sage, Mr. John George

male

?

1

9

CA. 2343

69.55

?

S

?

?

?

10

1173

3

0

Sage, Miss. Constance Gladys

female

?

8

2

CA. 2343

69.55

?

S

?

?

?

10

1170

3

0

Sage, Master. Thomas Henry

male

?

8

2

CA. 2343

69.55

?

S

?

?

?

10

1172

3

0

Sage, Miss. Ada

female

?

8

2

CA. 2343

69.55

?

S

?

?

?

10

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

598

2

1

Wright, Miss. Marion

female

26

0

0

220844

13.5

?

S

9

?

Yoevil, England / Cottage Grove, OR

0

599

2

0

Yrois, Miss. Henriette ('Mrs Harbeck')

female

24

0

0

248747

13

?

S

?

?

Paris

0

600

3

0

Abbing, Mr. Anthony

male

42

0

0

C.A. 5547

7.55

?

S

?

?

?

0

604

3

1

Abelseth, Miss. Karen Marie

female

16

0

0

348125

7.65

?

S

16

?

Norway Los Angeles, CA

0

1308

3

0

Zimmerman, Mr. Leo

male

29

0

0

315082

7.875

?

S

?

?

?

0

1309 rows × 15 columns

In [32]:

titanic[titanic["survived"] == 1].sort_values("num_relatives", ascending=False)

Out[32]:

 

pclass

survived

name

sex

age

sibsp

parch

ticket

fare

cabin

embarked

boat

body

home.dest

num_relatives

641

3

1

Asplund, Master. Edvin Rojj Felix

male

3

4

2

347077

31.3875

?

S

15

?

Sweden Worcester, MA

6

625

3

1

Andersson, Miss. Erna Alexandra

female

17

4

2

3101281

7.925

?

S

D

?

Ruotsinphyhtaa, Finland New York, NY

6

646

3

1

Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...

female

38

1

5

347077

31.3875

?

S

15

?

Sweden Worcester, MA

6

643

3

1

Asplund, Miss. Lillian Gertrud

female

5

4

2

347077

31.3875

?

S

15

?

Sweden Worcester, MA

6

111

1

1

Fortune, Miss. Alice Elizabeth

female

24

3

2

19950

263

C23 C25 C27

S

10

?

Winnipeg, MB

5

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

597

2

1

Williams, Mr. Charles Eugene

male

?

0

0

244373

13

?

S

14

?

Harrow, England

0

598

2

1

Wright, Miss. Marion

female

26

0

0

220844

13.5

?

S

9

?

Yoevil, England / Cottage Grove, OR

0

604

3

1

Abelseth, Miss. Karen Marie

female

16

0

0

348125

7.65

?

S

16

?

Norway Los Angeles, CA

0

605

3

1

Abelseth, Mr. Olaus Jorgensen

male

25

0

0

348122

7.65

F G63

S

A

?

Perkins County, SD

0

0

1

1

Allen, Miss. Elisabeth Walton

female

29

0

0

24160

211.3375

B5

S

2

?

St Louis, MO

0

500 rows × 15 columns

In [33]:

solo_passengers = titanic["num_relatives"] == 0titanic[solo_passengers].sex.value_counts().plot(kind="pie")

Out[33]:

<AxesSubplot:ylabel='sex'>

 

In [34]:

titanic.sex.value_counts().plot(kind="pie")

Out[34]:

<AxesSubplot:ylabel='sex'>

 

In [35]:

houses["price_sqft"] = houses["price"] / houses["sqft_living"]

In [36]:

houses

Out[36]:

 

county

id

date

num_bedrooms

price

bedrooms

bathrooms

sqft_living

sqft_lot

floors

...

sqft_basement

yr_built

yr_renovated

zipcode

lat

long

sqft_living15

sqft_lot15

sale_price

price_sqft

0

King County

7129300520

20141013T000000

3

221900.0

3

1.00

1180

5650

1.0

...

0

1955

0

98178

47.5112

-122.257

1340

5650

221900.0

188.050847

1

King County

6414100192

20141209T000000

3

538000.0

3

2.25

2570

7242

2.0

...

400

1951

1991

98125

47.7210

-122.319

1690

7639

538000.0

209.338521

2

King County

5631500400

20150225T000000

2

180000.0

2

1.00

770

10000

1.0

...

0

1933

0

98028

47.7379

-122.233

2720

8062

180000.0

233.766234

3

King County

2487200875

20141209T000000

4

604000.0

4

3.00

1960

5000

1.0

...

910

1965

0

98136

47.5208

-122.393

1360

5000

604000.0

308.163265

4

King County

1954400510

20150218T000000

3

510000.0

3

2.00

1680

8080

1.0

...

0

1987

0

98074

47.6168

-122.045

1800

7503

510000.0

303.571429

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

21608

King County

263000018

20140521T000000

3

360000.0

3

2.50

1530

1131

3.0

...

0

2009

0

98103

47.6993

-122.346

1530

1509

360000.0

235.294118

21609

King County

6600060120

20150223T000000

4

400000.0

4

2.50

2310

5813

2.0

...

0

2014

0

98146

47.5107

-122.362

1830

7200

400000.0

173.160173

21610

King County

1523300141

20140623T000000

2

402101.0

2

0.75

1020

1350

2.0

...

0

2009

0

98144

47.5944

-122.299

1020

2007

402101.0

394.216667

21611

King County

291310100

20150116T000000

3

400000.0

3

2.50

1600

2388

2.0

...

0

2004

0

98027

47.5345

-122.069

1410

1287

400000.0

250.000000

21612

King County

1523300157

20141015T000000

2

325000.0

2

0.75

1020

1076

2.0

...

0

2008

0

98144

47.5941

-122.299

1020

1357

325000.0

318.627451

21613 rows × 25 columns

In [37]:

houses.sort_values("price_sqft", ascending=False)

Out[37]:

 

county

id

date

num_bedrooms

price

bedrooms

bathrooms

sqft_living

sqft_lot

floors

...

sqft_basement

yr_built

yr_renovated

zipcode

lat

long

sqft_living15

sqft_lot15

sale_price

price_sqft

19336

King County

6021500970

20150407T000000

2

874950.0

2

1.00

1080

4000

1.0

...

0

1940

0

98117

47.6902

-122.387

1530

4240

874950.0

810.138889

4013

King County

724069059

20140509T000000

3

2400000.0

3

2.25

3000

11665

1.5

...

0

2001

0

98075

47.5884

-122.086

3000

15959

2400000.0

800.000000

10446

King County

1118000320

20150508T000000

4

3400000.0

4

4.00

4260

11765

2.0

...

980

1939

2010

98112

47.6380

-122.288

4260

10408

3400000.0

798.122066

8623

King County

6303400395

20150130T000000

1

325000.0

1

0.75

410

8636

1.0

...

0

1953

0

98146

47.5077

-122.357

1190

8636

325000.0

792.682927

9314

King County

4389200610

20141201T000000

2

903000.0

2

1.50

1140

7800

1.0

...

0

1947

0

98004

47.6142

-122.209

2020

7800

903000.0

792.105263

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

1385

King County

3342700465

20150123T000000

3

250000.0

3

1.50

2840

10182

1.0

...

1330

1951

0

98056

47.5240

-122.200

2210

9669

250000.0

88.028169

17197

King County

5111400086

20140512T000000

3

110000.0

3

1.00

1250

53143

1.0

...

0

1945

0

98038

47.4235

-122.051

1820

217800

110000.0

88.000000

13825

King County

5637200450

20141017T000000

5

257000.0

5

2.75

2930

10148

2.0

...

0

2002

0

98059

47.4887

-122.145

2930

8425

257000.0

87.713311

3785

King County

723049156

20140523T000000

3

149000.0

3

1.00

1700

8645

1.0

...

0

1955

0

98146

47.4899

-122.337

1500

7980

149000.0

87.647059

18262

King County

2891000610

20141211T000000

4

148900.0

4

1.75

1700

6000

1.0

...

0

1967

0

98002

47.3252

-122.208

1280

6000

148900.0

87.588235

21613 rows × 25 columns

In [38]:

houses.sort_values("price_sqft", ascending=False).head(50)["zipcode"].value_counts().plot(kind="bar")

Out[38]:

<AxesSubplot:>

 

In [39]:

btc.set_index("Date")

Out[39]:

 

High

Low

Open

Close

Volume

Marketcap

Date

 

 

 

 

 

 

2021-07-06 23:59:59

35038.536363

33599.916169

33723.509655

34235.193451

2.650126e+10

6.418992e+11

2021-07-05 23:59:59

35284.344430

33213.661034

35284.344430

33746.002456

2.672155e+10

6.326962e+11

2021-07-04 23:59:59

35937.567147

34396.477458

34665.564866

35287.779766

2.492431e+10

6.615748e+11

2021-07-03 23:59:59

34909.259899

33402.696536

33854.421362

34668.548402

2.438396e+10

6.499397e+11

2021-07-02 23:59:59

33939.588699

32770.680780

33549.600177

33897.048590

3.872897e+10

6.354508e+11

...

...

...

...

...

...

...

2013-05-03 23:59:59

108.127998

79.099998

106.250000

97.750000

0.000000e+00

1.085995e+09

2013-05-02 23:59:59

125.599998

92.281898

116.379997

105.209999

0.000000e+00

1.168517e+09

2013-05-01 23:59:59

139.889999

107.720001

139.000000

116.989998

0.000000e+00

1.298955e+09

2013-04-30 23:59:59

146.929993

134.050003

144.000000

139.000000

0.000000e+00

1.542813e+09

2013-04-29 23:59:59

147.488007

134.000000

134.444000

144.539993

0.000000e+00

1.603769e+09

2991 rows × 6 columns

In [40]:

btc["change"] = btc["Close"] - btc["Open"]

In [41]:

btc.sort_values("change", ascending=False)

Out[41]:

 

Date

High

Low

Open

Close

Volume

Marketcap

change

2842

2021-02-08 23:59:59

46203.931437

38076.322807

38886.827290

46196.463719

1.014672e+11

8.603427e+11

7309.636429

2919

2021-04-26 23:59:59

54288.002155

48852.796843

49077.792363

54021.754787

5.828404e+10

1.009780e+12

4943.962424

2863

2021-03-01 23:59:59

49784.015290

45115.093115

45159.503053

49631.241371

5.389130e+10

9.252355e+11

4471.738318

2853

2021-02-19 23:59:59

56113.650547

50937.275722

51675.981285

55888.133682

6.349550e+10

1.041381e+12

4212.152397

2923

2021-04-30 23:59:59

57900.719988

53129.600877

53568.663584

57750.177346

5.239593e+10

1.079670e+12

4181.513762

...

...

...

...

...

...

...

...

...

2911

2021-04-18 23:59:59

61057.456509

52829.535926

60701.886093

56216.185002

9.746887e+10

1.050445e+12

-4485.701091

2824

2021-01-21 23:59:59

35552.679497

30250.749639

35549.397409

30825.698506

7.564307e+10

5.735657e+11

-4723.698903

2857

2021-02-23 23:59:59

54204.929756

45290.590268

54204.929756

48824.426869

1.061025e+11

9.099259e+11

-5380.502887

2942

2021-05-19 23:59:59

43546.116485

30681.496912

42944.975447

37002.440466

1.263581e+11

6.924526e+11

-5942.534981

2935

2021-05-12 23:59:59

57939.362415

49150.533875

56714.533167

49150.533875

7.521540e+10

9.195278e+11

-7563.999292

2991 rows × 8 columns

In [42]:

btc["delta"] = btc["High"] - btc["Low"]

In [43]:

btc.sort_values("delta", ascending=False)

Out[43]:

 

Date

High

Low

Open

Close

Volume

Marketcap

change

delta

2942

2021-05-19 23:59:59

43546.116485

30681.496912

42944.975447

37002.440466

1.263581e+11

6.924526e+11

-5942.534981

12864.619573

2857

2021-02-23 23:59:59

54204.929756

45290.590268

54204.929756

48824.426869

1.061025e+11

9.099259e+11

-5380.502887

8914.339488

2935

2021-05-12 23:59:59

57939.362415

49150.533875

56714.533167

49150.533875

7.521540e+10

9.195278e+11

-7563.999292

8788.828540

2856

2021-02-22 23:59:59

57533.389325

48967.565188

57532.738864

54207.319065

9.205242e+10

1.010205e+12

-3325.419799

8565.824137

2944

2021-05-21 23:59:59

42172.173616

33616.453884

40596.948323

37304.690671

8.205162e+10

6.981088e+11

-3292.257651

8555.719732

...

...

...

...

...

...

...

...

...

...

871

2015-09-17 23:59:59

230.285004

228.925995

229.076004

229.809998

1.893540e+07

3.360247e+09

0.733994

1.359009

768

2015-06-06 23:59:59

225.718994

224.378998

225.005005

225.619003

1.113150e+07

3.213723e+09

0.613998

1.339996

759

2015-05-28 23:59:59

237.824005

236.651993

237.257004

237.408005

1.382960e+07

3.373817e+09

0.151001

1.172012

888

2015-10-04 23:59:59

238.968002

237.940002

238.531006

238.259003

1.299900e+07

3.499387e+09

-0.272003

1.028000

100

2013-08-07 23:59:59

106.750000

106.750000

106.750000

106.750000

0.000000e+00

1.229098e+09

0.000000

0.000000

2991 rows × 9 columns

In [44]:

btc.set_index("Date", inplace=True)

In [45]:

btc.sort_values("delta", ascending=False).head(10)["delta"].plot(kind="bar")

Out[45]:

<AxesSubplot:xlabel='Date'>